Stereotypes are thought to be an important determinant of decision making but are hard to systematically measure, especially for individuals in policy-making roles. In this paper, we propose and implement a novel language-based measure of gender stereotypes for the high-stakes context of U.S. Appellate Courts. We construct a judge-specific measure of gender-stereotyped language use – gender slant – by looking at the linguistic association of words identifying gender (male versus female) and words identifying gender stereotypes (career versus family) in the judge's authored opinions. Exploiting quasi-random assignment of judges to cases and conditioning on detailed biographical characteristics of judges, we study how gender stereotypes influence judicial behavior. We find that judges with higher slant vote more conservatively on women's rights’ issues (e.g. reproductive rights, sexual harassment, and gender discrimination). These more slanted judges also influence workplace outcomes for female colleagues: they are less likely to assign opinions to female judges, they are more likely to reverse lower-court decisions if the lower-court judge is a woman, and they cite fewer female-authored opinions.
Daniel Li Chen is Director of Research at the Centre National de la Recherche Scientifique (CNRS) and Professor at the Toulouse School of Economics. Chen was previously Chair of Law and Economics at ETH; he was Assistant Professor in Law (primary), Economics, and Public Policy at Duke University.
Chen received his BA (summa cum laude) and MS from Harvard University in Applied Mathematics and Economics; Economics PhD from MIT; and JD from Harvard Law School. He is the founder of oTree Open Source Research Foundation and Data Science Justice Collaboratory.